Current approaches to the development of automotive engine controllers are based largely upon analytical models that contain idealizations of engine dynamics as currently understood by automotive engineers. However, automotive engines are complicated systems, and many aspects of their dynamical behaviors are not yet well understood, thereby leading to inexact or incomplete engine models. The dynamics of each engine class varies in detail from one class to another, often resulting in dynamical behaviors that are apparently unique to a given engine class. In addition, model-based approaches to controller strategy development require that the actuators and sensors which form part of the engine system be appropriately characterized and included in the model from which a controller can be analytically synthesized.
Once a control strategy has been designed on the basis of an idealized model, the strategy is then calibrated by adjusting parameters, usually in the form of look-up tables, to achieve a desired performance or behavior. This calibration is usually performed by hand, which can be extremely time consuming considering the number of adjustable parameters (hundreds for idle speed control) that may be potentially adjusted. If the desired performance cannot be achieved via strategy calibration, the engine model is modified, a new or augmented strategy is synthesized, and the calibration for the new strategy is attempted. This cyclic process is repeated until the desired performance is achieved.